retrography

# MongoDB GridFile export
CSV::foreach('/Volumes/Content/Dropbox/Code/reuse/reimport/licenses/licensefiles.csv',{headers: true}) {|r| puts %x[mongofiles -d github -l #{r[1].gsub(/^\.\/([^\/]+)\/([^\/]+)\/.*/,'"\1\|\2"')} get "#{r[1]}"];}

retrography

import java.util.LinkedList;

public class LevenshteinDistance {

	public static final int NONE = 0;
	public static final int DELETE = 1;
	public static final int INSERT = 1;
	public static final int REPLACE = 2;
	
	private static int minimum(int a, int b, int c) {

retrography

#!/bin/bash
os=`uname`

# Python
if [[ "$os" == 'Linux' ]]; then
    apt-get install -y python python-pip python-dev #python3 python3-pip python3-dev
elif [[ "$os" == 'Darwin' ]]; then
    brew install python
fi

retrography

# First enable multiverse in /etc/apt/sources.list
# apt-get install revolution-r revolution-mkl

# From the horse's mouth
curl http://mran.revolutionanalytics.com/install/RRO-8.0.3-Ubuntu-14.04.x86_64.tar.gz | tar -xvz
curl http://mran.revolutionanalytics.com/install/RevoMath-8.0.3.tar.gz | tar -xvz
cd RRO*
./install.sh
cd ../RevoMath/
./RevoMath.sh

retrography

pip install bash_kernel

retrography

function createEdge (from, to, type) {
 
 var g = orient.getGraph();
 var cmd = "CREATE EDGE " + type + " FROM " + from + " TO " + to;
 
 return g.command("sql", cmd);

}

retrography

#! /usr/bin/env Rscript

lics<-read.csv("~/Temp/csvdb/license-union-norownum.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)

# Using dplyr

licsgr<-dplyr::group_by(lics, gem)
licsgrsum<-summarise(licsgr, list(license))
colnames(licsgrsum)<-c("gem", "licenses")

retrography

from pandas import DataFrame as df
import pandas as pd

lics = pd.read_csv("/Users/Mah/Temp/csvdb/license-union-norownum.csv")

licsgr = lics.groupby('gem')['license'].apply(list)
licsgr.columns = ['gem', 'licenses']

# But how do we convert Series to DataFrame in Pandas?

retrography

#!/bin/sh

tr '[A-Z]' '[a-z]' <<(echo "lOWER mE")

#$ lower me

retrography

library(igraph)
library(magrittr)
library(textir)
library(Matrix)

# fetch the network file
download.file("http://www-personal.umich.edu/~mejn/netdata/lesmis.zip", "lesmis.zip", "auto")
unzip("lesmis.zip")

g <- read.graph("lesmis.gml", format = "gml")